Automatic detection of pavement crack is an important task for conducting road maintenance. However, as an important part of the intelligent transportation system, automatic pavement crack detection is challenging due to the poor continuity of cracks, the different width of cracks, and the low contrast between cracks and the surrounding pavement. This study proposes a novel pavement crack detection method based on an end-to-end trainable deep convolution neural network. The authors build the network using the encoder–decoder architecture and adopt a pyramid module to exploit global context information for the complex topology structures of cracks. Moreover, they introduce a spatial-channel combinational attention module into the encoder–decoder network for refining crack features. Further, the dilated convolution is used to reduce the loss of crack details due to the pooling operation in the encoder network. In addition, they introduce a lovász hinge loss function, which is suitable for small objects. They train the authors' network on the CRACK500 dataset and evaluate it on three pavement crack datasets. Among the methods they compare, their method can achieve the best experimental results.

中文翻译：

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基于金字塔结构和注意机制的路面裂缝检测网络

自动检测路面裂缝是进行道路维护的重要任务。但是，作为智能交通系统的重要组成部分，由于裂缝的连续性差，裂缝的宽度不同以及裂缝与周围人行道之间的对比度低，自动路面裂缝检测是一项挑战。本研究提出了一种基于端到端可训练的深度卷积神经网络的新型路面裂缝检测方法。作者使用编码器-解码器体系结构构建网络，并采用金字塔模块来利用全局上下文信息获取复杂的裂纹拓扑结构。此外，他们在编码器-解码器网络中引入了空间通道组合注意模块，以完善裂纹特征。进一步，扩张卷积用于减少由于编码器网络中的合并操作而导致的裂纹细节损失。此外，它们还引入了lovász铰链损耗功能，该功能适用​​于小型物体。他们在CRACK500数据集上训练作者的网络，并在三个路面裂缝数据集上对其进行评估。在他们比较的方法中，他们的方法可以达到最佳的实验结果。